Diagnosis of many medical conditions requires the collection and analysis of medical data. In interpreting this data doctors and other medical personnel have generally applied a number of rules of thumb, or qualitative assessments, to reach their diagnosis. These rules of thumb have proven to be quite useful but are not comprehensive, for certain ailments and abnormalities cannot be adequately identified merely by applying currently established rules of thumb. One example where rules of thumb are applied is in monitoring electrocardiograph (EKG) data. EKG data is typically presented as a graphical output of a patient's heart activity. Doctors look for recognizable abnormalities and particular flags in the EKG data, as warning signals of health problems. They can discern certain abnormalities amongst this data by visually inspecting the graphical output; however, other important, more subtle abnormalities may go undetected. As such, the visual examination of data does not provide a complete diagnostic tool because some potentially significant abnormalities in the data are not v apparent from visual inspection.